Mapping Direct Seeded Rice in Raichur District of Karnataka, India
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Mapping Direct Seeded Rice in Raichur District of Karnataka, India Murail Krishna Gumma, Deepika Uppala, Irshad A. Mohammed, Anthony M. Whitbread, and Ismail Rafi Mohammed Abstract Across South Asia, the cost of rice cultivation has increased rice crop. Remote sensing technology provides a time saving due to labor shortage. Direct seeding of rice is widely pro- approach to estimate cropped area, intensity and other LULC moted in order to reduce labor demand during crop establish- changes in a country (Badhwar, 1984; Gumma et al., 2011a; ment stage, and to benefit poor farmers. To facilitate planning Gumma et al., 2015; Lobell et al., 2003; Thenkabail et al., and to track farming practice changes, this study presents 2009a; Thenkabail, 2010; Thiruvengadachari and Sakthiva- techniques to spatially distinguish between direct seeded and divel, 1997). Many studies have reported the use of spatial- transplanted rice fields using multiple-sensor remote sensing temporal data to map irrigated areas, land-use, land-cover, imagery. The District of Raichur, a major region in northeast and crop type (Dheeravath et al., 2010; Goetz et al., 2004; Karnataka, Central India, where irrigated rice is grown and Gumma et al., 2014; Knight et al., 2006; Thenkabail et al., direct seeded rice has been widely promoted since 2000, was 2005; Varlyguin et al., 2001; Velpuri et al., 2009) using MODIS selected as a case study. The extent of cropland was mapped NDVI time-series data to map both agricultural areas (Biggs et using Landsat-8, Moderate Resolution Imaging Spectroradi- al., 2006; Gaur et al., 2008; Gumma et al., 2011c) and seasonal ometer (MODIS) 16-day normalized difference vegetation index crop area (Sakamoto et al., 2005). There are also studies that (NDVI) time-series data and the cultivation practice delineated have used Synthetic Aperture Radar (SAR) data to monitor using RISAT-1 data for the year 2014. Areas grown to rice were rice areas, particularly irrigated rice. (Le Toan et al., 1997) mapped based on the length of the growing period detected used ERS-1 data as input to crop growth simulation models to using spectral characteristics and intensive field observa- estimate production for study sites in Indonesia and Japan. tions. The high resolution imagery of Landsat-8 was useful to Similarly, (Shao et al., 2001) mapped rice areas using tempo- classify the rice growing areas. The accuracy of land-use/land- ral RADARSAT data (1996 and 1997) for production estimates cover (LULC) classes varied from 84 percent to 98 percent. The in Zhaoqing in China. (Imhoff and Gesch, 1990) derived results clearly demonstrated the usefulness of multiple-sensor sub-canopy digital terrain models of flooded forest using SAR. imagery from MOD09Q1, Landsat-8, and RISAT-1 in mappingDelivered the by Ingenta(Kasischke and Bourgeau-Chavez, 1997) monitored the wet- rice area and practices accurately, IP:routinely, 192.168.39.211 and consistently. On: Mon, lands27 Sep of South2021 21:14:55Florida using ERS-1 SAR imagery. (Leckie, 1990) The low cost of imagery backedCopyright: by ground American survey, Society as dem for- Photogrammetrydistinguished and forest Remote type usingSensing SAR and optical data. (Robin- onstrated in this paper, can also be used across rice growing son et al., 2000) delineated drainage flow directions using SAR countries to identify different rice systems. data. (Townsend, 2001) mapped seasonal flooding in forested wetlands using temporal RADARSAT SAR imagery. (Bouvet and Le Toan, 2011) demonstrated how lower resolution wide- Introduction swath images from advanced SAR (ASAR) data could be used to Agriculture is an important sector in India, contributing about map rice over larger areas. (Uppala et al., 2015) mapped rice 17.9 percent of the gross domestic product (GDP) (2014 figures) areas using single data hybrid polarimetric data from RISAT-1 (CIA, 2015). About 68 percent of the country’s population lives SAR data. The advantage of rice mapping with SAR is that it in rural areas (FAOSTAT, 2013). India is one of the largest rice can overcome pervasive cloud cover across Asia during the growing nations accounting for one quarter of the global rice rainy season months when rice is cultivated. However, its area and produces around 125 million tons/year, with low high cost inhibits its large-scale application. yields of around 2.85 t/ha (Siddiq, 2000). Given that rice is a A comprehensive plan to increase agricultural productivity staple food crop in India and that the country has a popula- and manage labor can be drawn up based on near real-time tion that is growing at 1.2 percent per annum, a substantial monitoring of rice systems along with weather and crop data increase in rice production is the only way to guarantee food acquired from an analysis of remote sensing imagery. Remote security and to keep food prices low. Labor and fertilizer are sensing platforms have proved to be ideal to take resource the two major input costs that are incurred while growing rice inventories and monitor agriculture. Interdisciplinary ap- when there is assured water availability (Savary et al., 2005). proaches and methods have made it easier to analyze imagery Migration from rural to urban areas in search of work has led and extract information in ways unknown a few decades ago. to labor shortages and higher costs of agricultural operations The goal of this study was to develop a method using mul- (Hossain, 1998; Savary et al., 2005). To overcome these con- tiple-sensor imagery (Landsat-8, RISAT-1 and MODIS time-series straints, promoting direct seeding in lieu of the traditional and data) to map the spatial extent of two different rice cultiva- labor-intensive transplanting (De Datta, 1986; Naylor, 1992) is tion practices (direct seeding and transplanting) in Raichur being seen as a feasible alternative. However, there is a trad- district, Karnataka State, India. eoff with lower yields in rice that is direct seeded rather than transplanted; so efforts are on to bridge this gap (Khush, 1995). Accurate and timely information on rice systems, cropped area, and yields are very important for sustainable food secu- Photogrammetric Engineering & Remote Sensing rity. Several studies have used remote sensing data to monitor Vol. 81, No. 11, November 2015, pp. 873–880. 0099-1112/15/873–880 International Crops Research Institute for the Semi-Arid © 2015 American Society for Photogrammetry Tropics (ICRISAT), Patancheru, Telangana, India (m.gumma@ and Remote Sensing cgiar.org). doi: 10.14358/PERS.81.11.873 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING November 2015 873 11-15 November Peer Reviewed.indd 873 10/21/2015 1:18:30 PM Study Area and Data Sets Ground Survey Data A ground survey was conducted during the monsoon season Study Area for the crop year 2014 to 2015. Data was collected at 238 loca- Raichur is one of largest and most productive rice growing tions covering the major cropland areas in the study area. All district in Karnataka state in southern India (Figure 1). The location-specific data were collected from 250 × 250 meter district lies between 15°42'57" and 16°26'39" latitude and plots and consisted of location coordinates (latitude, longi- 76°14'47" and 77°35'39" longitude, with a land mass of 835,843 tude), land-use categories, land-use (percent), cropping systems ha (cropped area 695,000 ha) (Figure 1) and bound in the during the monsoon season (through farmer interviews), crop north and south by the rivers Krishna and Tungabhadra. Agro- types, and watering method (irrigated/rainfed). Samples were climatically, it falls under the Northeast Dry Zone of Karnataka within large contiguous areas of a specific land-use/land-cover. (semi-arid eco-subregion) with a long term average rainfall of The locations were chosen based on pre-classification classes just above 600 mm distributed over kharif and rabi seasons. and local expertise. The local experts also provided information Much of the district is well irrigated by the Tungabhadra Dam on cropping systems, cropping patterns and cropping inten- on the Tungabhadra River, and the Narayanpura Dam on the sity (single or double crop), irrigation application, and canopy Krishna River, covering an irrigated area of 185,000 ha, 72.2 cover ( percent) for the previous years for these locations from percent of which is from canals. The arable rainfed area occu- their recorded data. Overall, 116 spatially well-distributed data pies 405,000 ha. The major crops grown in the district include points (Figure 1) were used for class identification and labeling; paddy, sorghum, groundnut, sunflower, cotton, and pulses of these, 46 data points were used for ideal spectra generation (chickpea and pigeonpea). The rice-rice cropping system is and an additional 122 were used to assess accuracy. dominant in most irrigated areas. The soils of the district are mainly deep black clayey (47 percent) and red (34 percent). TABLE 1. CHARACTERIstICS OF MULTIPLE SATELLITE SENSOR DATA USED IN THE STUDY Satellite Images Spatial Band range Four Landsat-8 tiles were downloaded from the Earth Ex- Sensor Dates Bands (m) (µm) plorer, global land cover facility website (http://earthexplorer. usgs.gov/). Images from the 2014 monsoon season and their 1 0.433–0.453 spectral characteristics are shown in Table 1. All the Landsat-8 2 0.450–0.515 tiles were converted into reflectance http://landsat.usgs.gov/( documents/Landsat8DataUsersHandbook.pdf) to normalize 3 0.525–0.600 07 & 14 the multi-date effect (Markham and Barker, 1986; Thenkabail Landsat-8 30 4 0.630–0.680 et al., 2004) using the spatial modeler in ERDAS Imagine® (ER- Nov 2014 5 0.845–0.885 DAS, 2007). The 250-m, two-band MODIS data (centered at 648 nm and 858 nm; Table 1) collection 5 (MOD09Q1) were acquired 6 1.560–1.660 for every eight days during the crop-growing seasons from 7 2.100–2.300 January 2014 through December 2014.